Mine fleet cost evaluation - Dijkstra's optimized path

Abstract The transport distance in a mining operation strongly influences a mine operation revenue and its operational cycle because it is a fundamental part of the total mining costs. Generally, the transport route is determined based on an engineer's practical knowledge, which does not consider any mechanism to optimize the possible routes to be taken. In an attempt to establish a methodology for calculating the path that results in minimum costs to transport the mined block to its destination, the Dijkstra methodology is applied to a tree graph analysis, where the mining blocks are analysed as nodes of the tree. The transport cost is reflected as the arc of the graphs, which can use the Euclidean distance or the transport time for the calculation of the minimum path. The result obtained from the Dijkstra algorithm provided a non-operational route; to overcome this problem, an adjustment was performed through non-parametric equations. In this manner, it was possible to determine the transport costs for each block of the model. The paths based on Euclidean distance and transport time showed a tendency to increase for deeper mining regions. Identifying areas of largest growth and correctly quantifying their values increase the efficiency of mining planning.

摘要：采矿作业中的运输距离是总采矿成本的核心构成部分，因此对矿山运营收益及作业周期影响显著。常规情况下，运输路线的制定仅依托工程师的实践经验，未引入任何可优化备选运输路径的机制。为构建一套可计算将开采区块运抵目的地的最小成本路径的方法，本研究将迪杰斯特拉方法（Dijkstra methodology）应用于树状图分析，将采矿区块作为树状结构的节点进行分析。运输成本以图的弧段表征，可通过欧氏距离（Euclidean distance）或运输时长来计算最小路径。由迪杰斯特拉算法得到的结果存在不具备作业可行性的路径问题，为解决该缺陷，研究通过非参数方程进行了调整优化。借此方式，可确定模型中每个采矿区块的运输成本。基于欧氏距离和运输时长的运输路径，在开采深度更大的区域均呈现出增长趋势。识别增长幅度最高的区域并准确量化其数值，可有效提升采矿规划的效率。